{"id":"W4296465269","doi":"","title":"Multi-scale modelling as a tool for sharing the perspectives of researchers, practitioners and farmers on beneficial management practices to be adopted in an intensive agricultural watershed","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Agriculture; Scale (ratio); Environmental resource management; Business; Knowledge management; Environmental planning; Agricultural management; Computer science; Environmental science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01082691,0.0008201083,0.001022312,0.001902988,0.001337715,0.005299232,0.002441356,0.002951499,0.00853427],"category_scores_gemma":[0.01583026,0.0007573449,0.001706159,0.002133432,0.001156648,0.005907755,0.007963995,0.002299943,0.000781477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001964521,"about_ca_system_score_gemma":0.0025559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004011129,"about_ca_topic_score_gemma":0.006476812,"domain_scores_codex":[0.995868,0.002786165,0.0003176685,0.0003435367,0.000537654,0.0001470313],"domain_scores_gemma":[0.9816486,0.01310704,0.000827102,0.002895904,0.0008746017,0.0006467952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003786133,0.0005957347,0.006904175,0.0006833412,0.0003967741,0.0005040515,0.008714262,0.7288185,0.009364812,0.1461232,0.00644098,0.09107558],"study_design_scores_gemma":[0.0001345655,0.0001223008,0.001345557,0.0002096501,0.00008440221,0.00007406083,0.00217496,0.8305867,0.002077991,0.1363502,0.02671899,0.0001205917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04165911,0.0001199805,0.9347376,0.00284461,0.0001648102,0.00030883,0.0008324183,0.002927471,0.01640521],"genre_scores_gemma":[0.3844105,0.0001903136,0.6101473,0.0002275193,0.00003968804,0.001028732,0.0007840391,0.0005016436,0.002670383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01082691,"threshold_uncertainty_score":0.05725878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0486331266286637,"score_gpt":0.2952635489079671,"score_spread":0.2466304222793034,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}